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Visualization and Graphical Outlook on LST Data (ROI Analysis)\n","Created by: Jason Zhang\n","\n","(Detailed information in readme.md)\n","\n","The code below initializes earth engine. Project name is stored in `projName`. Running this will ask for authentication."],"metadata":{"id":"yEMe5IHbu3mW"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"HJ8sCYa8rVPh","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1740322719292,"user_tz":300,"elapsed":221026,"user":{"displayName":"TeamMcPro","userId":"15171345446647417487"}},"outputId":"6b4f86d8-cde6-49a6-b9df-a717b0de8de9"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.6 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.3/1.6 MB\u001b[0m \u001b[31m9.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m25.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m18.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h"]}],"source":["!pip install -q earthengine-api\n","!pip install -q geemap\n","\n","# Earth Engine Imports\n","import ee\n","import geemap\n","\n","from datetime import datetime # for ease of storing dates\n","import numpy as np\n","import matplotlib.pyplot as plt\n","\n","projName = '' # insert your project name\n","\n","ee.Authenticate()\n","ee.Initialize(project=projName)\n","\n","unsafe = False # for testing experiemental features, keep false for regular experience"]},{"cell_type":"markdown","source":["The code below sets up `eeLSTData` and `eeMap`, two classes made to efficently store data for analysis. The class `eeLSTData` is responsible for keeping track of LST information within a specific region within a specific time frame. This is utilized by the `eeMap` class for visualization of LST data.\n","\n","The `eeLSTData` constructor asks for `cords`, the start date, and end date. Note that it will NOT give you information throughout the entirety of these dates, only the most recent one within the time frame (closest to the end date). Formatting for dates is `YYYY-MM-DD`. The process method obtains `ST_B10` band information while masking cloud data. It returns a tuple of three elements, the Celsius data, the image date, and the raw collection. Clouds are masked before data is processed.\n","\n","The `eeMap` class is used for storing multiple `eeLSTData` and being able visualize all of them in the same map. This utilizes the `geemap` feature. It is heavily recommended to use the `addData` for adding LST data. Doing it manually may break the code or cause unexpected behavior."],"metadata":{"id":"P21NEUOPQdzW"}},{"cell_type":"code","source":["\"\"\"\n","Global Region of Interest is formatted as (x1, y1, x2, y2).\n","This ROI is used for many\n","\"\"\"\n","globalROI = [\n","              -77.55886136745653,\n","              39.08850478643373,\n","              -77.41670634220205,\n","              38.908230452449686\n","            ]\n","\n","class eeLSTData(object):\n","  # Constructor method\n","  def __init__(self, cords=globalROI, stime=\"2023-08-31\", etime=\"2024-08-31\"):\n","    self.cords = cords # (x1, y1, x2, y2)\n","    self.time = [stime, etime] # start time and end time for data collection area, should seek etime for closer approximation\n","    self.roi = ee.Geometry.Rectangle(self.cords)\n","    self.imageDate = None\n","\n","  def _mask(self, image): # This is for masking clouds. The qa_pixel returns retain values that indicate confidence for clouds. We filter clouds out.\n","    qa_pixel = image.select('QA_PIXEL')\n","    cloud_mask = qa_pixel.bitwiseAnd(1 << 3).eq(0)\n","    return image.updateMask(cloud_mask)\n","\n","  def process(self): # Returns tuple of data related to LST data in C\n","    landsat_collection = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2') \\\n","                            .filterBounds(self.roi) \\\n","                            .filterDate(self.time[0], self.time[1])\n","    try:\n","      if landsat_collection.size().getInfo() == 0:\n","        return (None, None, None) # This is a failsafe in case no data was avaiable in the time interval.\n","    except:\n","      print(self.time) # Prints time out in case of a bug. This should not ever run.\n","\n","    landsat_collection = landsat_collection.map(self._mask) # mask clouds first\n","\n","    most_recent_image = landsat_collection.sort('system:time_start', False).first()\n","\n","    image_date = ee.Date(most_recent_image.get('system:time_start')).format('YYYY-MM-dd').getInfo()\n","    self.imageDate = image_date # Logs image date\n","\n","    landsat_thermal = landsat_collection.select('ST_B10').median()\n","\n","    lst_celsius = landsat_thermal.multiply(0.00341802).add(149).subtract(273.15) # Rescale into C\n","\n","    return (lst_celsius, image_date, landsat_collection)\n","\n","  def __repr__(self):\n","    return f\"LST Visual at {self.imageDate}\"\n","\n","class eeMap(object):\n","  def __init__(self, cords=globalROI, z1=10):\n","    self.cords = cords # Cords are formatted as [x1, y1, x2, y2]\n","    self.roi = ee.Geometry.Rectangle(self.cords)\n","    self.center = [(self.cords[0] + self.cords[2])/2, (self.cords[1] + self.cords[3])/2][::-1] # Center for the map, simply the average of the cords\n","    self.zoom = z1\n","    self.map = geemap.Map(center=self.center, zoom=self.zoom)\n","    self.map.add_basemap('SATELLITE')\n","    self.lst_vis_params = {\n","      'min': -20,  # Minimum temperature in Celsius\n","      'max': 50,   # Maximum temperature in Celsius\n","      'palette': ['blue', 'cyan', 'green', 'yellow', 'red']  # Color scale for visualization\n","    }\n","    self.vis_params = {\n","    }\n","    self.LST = [] # LST data\n","\n","  def addLSTDataManual(self, data : eeLSTData):\n","    temp = data.process()\n","    if temp[0] == None:\n","      return False # If no data was avaiable\n","    self.map.addLayer(temp[0], self.lst_vis_params, data.__repr__()) # Adds map with the data.\n","    return True\n","\n","  # Intermediate method so all LST is logged within self.LST. Skipping this will break code.\n","  def addLSTData(self, sdate, edate):\n","    self.LST.append(eeLSTData(stime=sdate, etime=edate))\n","    return self.addLSTDataManual(self.LST[-1])\n","\n","  \"\"\"\n","  This is the recommended function to use.\n","  Specify start date and end date.\n","  \"\"\"\n","  def addData(self, sdate, edate):\n","    m = self.addLSTData(sdate, edate)\n","    if m == False:\n","      print(f\"Failed on {sdate}, {edate}\") # failure data\n","      return False\n","    if unsafe: # This runs experiemental code.\n","      collection = ee.ImageCollection(\"LANDSAT/LC08/C02/T1_L2\") \\\n","          .filterBounds(self.roi) \\\n","          .filterDate(sdate, edate)\n","\n","      image = collection.first()\n","      image_date = ee.Date(image.get('system:time_start')).format('YYYY-MM-dd').getInfo()\n","      self.map.addLayer(image, self.vis_params, f\"Map of {image_date}\")\n","    return True\n","\n","Map = eeMap()\n","\n","Map.addData('2014-01-01', '2014-08-31') # example dates\n","Map.addData('2024-01-01', '2024-08-31') # YYYY-MM-DD\n","\n","Map.map # visualize the map"],"metadata":{"id":"utL2ZvS1vDy1","colab":{"base_uri":"https://localhost:8080/","height":621,"referenced_widgets":["13e920428f3b43889648ce02b55b0fb8","dfecd2fa6736494ea566c96f81b14845","b43bdd70b79146058960762cfcbb0c4a","c0be99f701314aebb676e41cdca07c66","3af874111c944c12bc4f46eced13d8c3","3f3bc3175a49422db87fbad15c230994","bf71858a1d5f4cc8a2f67aa5efc9ed9b","df4c1836d9ce4024b68d013e2d43048b","7cafd0d707f748209ee2d7dff6e74471","5e046c33c0f340abab70d484108e1d5e","457d720e642b45439ca89cf789127f38","7d16a69cb60a4cf0a1bf1897cdc598de","61fe192b8e8c47b49204f3c497ed0389","b92440f3b1774b1ba4ee44a77d6e7c64","2e0dbd84c2274f4691b00c4dd7408b4d","ec517e94ffa549148867519bfcba6abd","bd9e4cbde6cf4a1f92a6e10e50b277df","46b9fc585b3d45cb809285933d50224e","9145f41f4fab48c7afb0e54c5b18d91a","f0cfdc5647374f1aa0350f1ce2c7218f","32040a2c35734eb689d098b071005f68","ee3892fe3b0c4f97a6ee8f2e90f7ede7","dd896076e50c409e8f62222730d8cc47","e1afb6e03f284d55a1638879d92213ca","0f1fa189aaf44f80bb0c4baf83e5886b","682519f5950f4143a4683e7638f2a796","81a1f662702f4db1a23a4ff438237bc2","320f7abfa73146a8a9a1dbcfcdafea36","a81c7ef424c64cb0afc05ac688b723ab","40e0de3d34d545e5a8aeae643b0a0067","e40728ae096d4380ab48d1f2be3fec5d","95f62514288048fbb3421e873300d053","af35cad754214d159bbdbd7b5a7eafe9","a251b5fb09dc4c8fbc58fd71fe099902","3260580f9f1543efbec8e747b9b5f59b","43c83a2ad89d41b8b9654332e6f0050b","61a4fcd0a29a428db7387b1437bdb7f0","7fbab60e3383486aa24495d887bd854d","c4534fe3cf9442d39232a9305b45e243","ff715c04925340b58926495f59484a49","89bf18d25e5c481bad67fae5be47111a","e8f42f76c379499abd6f763219f0e09a","7f730fcd5c174c8e8957b609402b7ae1","0f02595892314ac3af231bb8ef37c02c","f4028ee70e2145dba95a1a8a877c590d","e82560ffa95d48d98905a85c0ac3d265","b80a9f45618a4d268bdc2a48f91dce3e","e5174f1e80e549a4821c9812e6d41ec5","ab6a6f3dbc3444d6844af79b30c87eb3","126db085bb9a483dada2f5afd2feaac6","29916206a02948bf9e854b406f7257be","7160f53237fb437985a13a1ea899aca5","dd1703fdc8a742e68583d1df1d7431bf","9762ee89c72744769af0161a397b6e3c","e58322a01e674047889f68ff27639031","9970b5c84b254ca4b0052ded5c1070f3","25c1f15aa2a14c099bb6c2e01290224b","8a50000e57194a1e82cdb86765c1d19c","5a78038d352b47629b7b86b883439830","61ae651c50c64d8b9a35652394b1a890","112077f8c5b949809c2a6e87b8ebcd2e","0331d526660d4632b679742ac2005035","fd6726b570444ed7b35715334a545a6b","c20edc46d75641118a1cc389c8b2d9d9","6deb58f0bfbc4857893cd9d98a305471","8f99150575bc4aefa33128927c5ce4d2","b1f918e89bc44f68b834c390ab441523","28323b8467054abe99b4aa0d5ecbc990","16ac4be68991439baa00f2dc86e1aa92","776831bafb7140548b9e9a2423d1711e","905dbcd99a764c27872fcc0f92dc48e1","41e9000eef8c4170a57a404205a7e179","f97231382311479b8544b76f68355ea5","ce2a56af064c4e32a1984b0db8a2717e","13e356ffe28f4e13b870fbbf16dd132c","bbdd1483a96141ed8780df94d53bddb6","c429aa6090614d04baaf170e5e1c1323","9f4d5c8daa5f42e4a8d8384c43c7c0ea","020c169766da4afab031c29873ad59b7","8b39b23cb75d43bf965b11a58d474bca","0da49a9bdd2a4f9da579f71e0ed0b3f3","ef4e4fd8b22a4877b045d629c2a5fa2e","3b66756363e143c19857234aaec33a4f","42866618e4de43d38fb8f59eb3feb7be","1b8a888842f048f0bec5b34938a63c79","5846624617c34a97a090fb23cb5098bf","84bbe6c3ec6f4925ad204327974690ae","d7734e5cb61f457fa4d4e2906793dd94","cf465a615d84448c97bcef147dfb81f1","b6e874c0bf674020bcd5b75d93839f36","bfb158ff88d6402bb45aee3d53db21c4","31355c286433473598b4de4a8889c6b6","f78aad0f1f3c44a28501e10f960fd077","614848c1b0c34b88a8f5695390619f63","7c4b88740ffa4e1cbf7c0a4854282622","986b1942b3714293a2e471e58a04e213","ee13c3cb1252496f97e1bf5e51459d5c","5ecb81a8720048b4b4219db9cfcb36da","0f687395bfc140ecbe51227d70724644","a769aad6f64b4d238d186411836b09ed","789631b7c6744f46a9323f9069ef0984","3c8f83f2075a4ebab614b5a23d7b261b","4fad952d287a4ade81b2ef31fa9dff0c","be2bffc892924b23bc68d8f35eac2ca4","d27339d2dbcc482d825adde1550ee2bb"]},"executionInfo":{"status":"ok","timestamp":1740322754226,"user_tz":300,"elapsed":1902,"user":{"displayName":"TeamMcPro","userId":"15171345446647417487"}},"outputId":"db996b6c-782d-4a0d-93cd-e4d6fdb73e44"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","            <style>\n","                .geemap-dark {\n","                    --jp-widgets-color: white;\n","                    --jp-widgets-label-color: white;\n","                    --jp-ui-font-color1: white;\n","                    --jp-layout-color2: #454545;\n","                    background-color: #383838;\n","                }\n","\n","                .geemap-dark .jupyter-button {\n","                    --jp-layout-color3: #383838;\n","                }\n","\n","                .geemap-colab {\n","                    background-color: var(--colab-primary-surface-color, white);\n","                }\n","\n","                .geemap-colab .jupyter-button {\n","                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n","                }\n","            </style>\n","            "]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map(center=[38.99836761944171, -77.48778385482929], controls=(WidgetControl(options=['position', 'transparent_…"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"13e920428f3b43889648ce02b55b0fb8"}},"metadata":{"application/vnd.jupyter.widget-view+json":{"colab":{"custom_widget_manager":{"url":"https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"}}}}}]},{"cell_type":"markdown","source":["The code below is for analyzing the `eeMap` and `eeLSTData`. Check each class for specific constructor data required.\n","\n","The outine of the code is that each `Analysis` class takes in either a map or start/end date. Specifically, `BaseAnalysis` for general data processing, `IntervalBaseAnalysis` for comparing two time periods, `MeanBaseAnalysis` for calculating mean temperatures, `MedianPointAnalysis` for pixel-level median computations, and `DifferentialAnalysis` for detecting temperature changes.\n","\n","\n","\n","\n","\n","\n"],"metadata":{"id":"lXrl5e4aRz5s"}},{"cell_type":"code","source":["# A function used to convert a number to a date.\n","def numToDate(num, year):\n","  day_num = str(num)\n","  day_num.rjust(3 + len(day_num), '0')\n","  res = datetime.strptime(year + \"-\" + day_num, \"%Y-%j\").strftime(\"%Y-%m-%d\")\n","  return str(res)\n","\n","\"\"\"\n","Analysis Classes require either a map or time to be provided.\n","In this case, an eeMap is required.\n","Map must already have eeLSTData built in.\n","\"\"\"\n","class BaseAnalysis(object):\n","  def __init__(self, map : eeMap, bins=50):\n","    self.map = map\n","    self.bins = bins\n","    if self.map.LST == []:\n","      raise BaseException(\"No valid map provided!\")\n","    self.LST = self.map.LST\n","    self.dataSets = []\n","    self.dates = []\n","    self.data = dict() # The finalized data to display\n","    self.dataPrep()\n","    self.analyze()\n","\n","  # Data preperation, adds info to selfdataSets and corresponding dates to self.dates. Prepares both for the self.analyze method.\n","  def dataPrep(self):\n","    for i in range(len(self.LST)):\n","      curr = self.LST[i]\n","      info = curr.process()\n","      self.dataSets.append(np.array(info[0].reduceRegion(\n","          reducer=ee.Reducer.toList(),\n","          geometry=self.map.roi,\n","          scale=100,\n","          maxPixels=1e13\n","      ).get('ST_B10').getInfo()))\n","      self.dates.append(info[1])\n","\n","  # Plots the data\n","  def visualize(self, info): # 'info' is formatted as [title, xaxis, yaxis]\n","    plt.figure(figsize=(10, 6))\n","    tempVar = list(self.data.keys())\n","    for i in range(len(tempVar)):\n","      # This plots all data avaliable through the self.data dict. Note that the value is plotted as actual data, while the key is the label.\n","      plt.hist(self.data[tempVar[i]], bins=self.bins, color=(1 / (i ** 2 + 2), 1 / (i + 1), 1 - 1 / (i + 1)), alpha=0.7, label=tempVar[i], edgecolor='black')\n","\n","    plt.title(info[0])\n","    plt.xlabel(info[1])\n","    plt.ylabel(info[2])\n","\n","    # Add grid\n","    plt.grid(True)\n","\n","    # Add legend with location\n","    plt.legend(loc='upper right')  # The legend will show labels with corresponding colors\n","\n","    # Show the plot\n","    plt.show()\n","\n","  def analyze(self): # This analyze function simply adds all the data into self.data. This is because BaseAnalysis has no special functionality.\n","    for i in range(len(self.dates)):\n","      self.data[self.dates[i]] = self.dataSets[i]\n","\n","# This is for measuring an interval of dates\n","# By default it measures median\n","class IntervalBaseAnalysis(BaseAnalysis):\n","  def __init__(self, year1, year2, dayS=1, dayE=365, bins=50, prune=True): # dayS and dayE are start and end date within a year respectively\n","    self.year1 = str(year1)\n","    self.year2 = str(year2)\n","    self.dayS = dayS\n","    self.dayE = dayE\n","    self.bins = bins\n","    self.prune = prune # This is the feature that removes some lower outliers. Set it to false to include all data.\n","    self.LST1 = eeLSTData(stime=numToDate(dayS, self.year1), etime=numToDate(dayE, self.year1))\n","    self.LST2 = eeLSTData(stime=numToDate(dayS, self.year2), etime=numToDate(dayE, self.year2))\n","\n","    self.map = eeMap() # Dummy variable, unused.\n","    self.LST = self.map.LST # Unused.\n","    self.dataSets = []\n","    self.dates = []\n","    self.data = dict() # to display\n","    self.analyze()\n","\n","  def analyze(self):\n","    # Runs an analysis on year 1 and 2.\n","    self.data[self.year1] = np.array(self.smallAnalysis(self.LST1, numToDate(self.dayS, self.year1), numToDate(self.dayE, self.year1)))\n","    self.data[self.year2] = np.array(self.smallAnalysis(self.LST2, numToDate(self.dayS, self.year2), numToDate(self.dayE, self.year2)))\n","\n","  def smallAnalysis(self, LST, time1, time2):\n","    dataset = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')\\\n","    .filterBounds(LST.roi)\\\n","    .filterDate(time1, time2)\n","\n","    def get_date(image):\n","      return image.set('date', ee.Date(image.get('system:time_start')).format('YYYY-MM-dd'))\n","\n","    unique_dates = dataset.distinct(['system:time_start']).map(get_date).aggregate_array('system:time_start').getInfo()\n","\n","    # Prepare the list to store median temperatures\n","    median_temps_celsius = []\n","\n","    scale_factor = 0.00341802 # Scale factor from K -> C\n","\n","    # Iterate through each date\n","    for date in unique_dates:\n","        # Filter the dataset to the specific date\n","        daily_collection = dataset.filterDate(date, ee.Date(date).advance(1, 'day'))\n","\n","        # Compute the median image\n","        median_image = daily_collection.median() # [a1, a2, a3, a4...] -> a2\n","\n","        # Extract LST band\n","        median_temp_kelvin = median_image.reduceRegion(\n","            reducer=ee.Reducer.median(),\n","            geometry=LST.roi,\n","            scale=30,  # Scale in meters, adjust based on resolution\n","            maxPixels=1e9\n","        ).get('ST_B10')  # Replace with the actual LST band name\n","\n","        if median_temp_kelvin is not None:\n","            median_temp_kelvin = ee.Number(median_temp_kelvin).multiply(scale_factor).add(149).getInfo()\n","            median_temp_celsius = median_temp_kelvin- 273.15  # Convert Kelvin to Celsius\n","            if self.prune: # This prunes outliers where the data may not have made sense, example: -30 degrees. This can be disabled.\n","              if float(median_temp_celsius) < -20:\n","                continue\n","            median_temps_celsius.append(float(median_temp_celsius))\n","    return median_temps_celsius\n","\n","class MeanBaseAnalysis(IntervalBaseAnalysis):\n","  \"\"\"\n","  IMPORTANT!\n","  Code here still still has \"median\" instead of \"mean\" in variable names. Functionality is changed but the variable names are still median for consistency.\n","  \"\"\"\n","  def smallAnalysis(self, LST, time1, time2):\n","    dataset = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')\\\n","    .filterBounds(LST.roi)\\\n","    .filterDate(time1, time2)\n","\n","    def get_date(image):\n","      return image.set('date', ee.Date(image.get('system:time_start')).format('YYYY-MM-dd'))\n","\n","    unique_dates = dataset.distinct(['system:time_start']).map(get_date).aggregate_array('system:time_start').getInfo()\n","\n","    # Prepare the list to store mean temperatures\n","    median_temps_celsius = []\n","\n","    scale_factor = 0.00341802 # Scale Factor for K -> C\n","\n","    # Iterate through each date\n","    for date in unique_dates:\n","        # Filter the dataset to the specific date\n","        daily_collection = dataset.filterDate(date, ee.Date(date).advance(1, 'day'))\n","\n","        # Compute the median image\n","        median_image = daily_collection.mean()\n","\n","        # Extract LST band\n","        median_temp_kelvin = median_image.reduceRegion(\n","            reducer=ee.Reducer.mean(),\n","            geometry=LST.roi,\n","            scale=30,  # Scale in meters, adjust based on resolution\n","            maxPixels=1e9\n","        ).get('ST_B10')  # Replace with the actual LST band name\n","\n","        if median_temp_kelvin is not None: # Checking eeNumber\n","            median_temp_kelvin = ee.Number(median_temp_kelvin).multiply(scale_factor).add(149).getInfo()\n","            median_temp_celsius = median_temp_kelvin - 273.15  # Convert to C\n","            if self.prune:\n","              if float(median_temp_celsius) < -20:\n","                continue\n","            median_temps_celsius.append(float(median_temp_celsius))\n","    return median_temps_celsius\n","\n","class MedianPointAnalysis(IntervalBaseAnalysis):\n","  def smallAnalysis(self, LST, time1, time2):\n","    # Step 1: Use the Landsat 8 Surface Temperature dataset\n","    lst_collection = ee.ImageCollection(\"LANDSAT/LC08/C02/T1_L2\").select('ST_B10')\n","\n","    # Step 2: Filter the dataset for the time frame and compute the median\n","    time_filtered = lst_collection.filterDate(time1, time2).filterBounds(LST.roi)\n","    median_lst = time_filtered.median().clip(LST.roi) # time_filtered = [pixel1: [data from year1-year2], pixel2:[...],]\n","\n","    # Step 3: Reduce the image to a list of pixel values\n","    scale = 30  # Approximate scale in meters for Landsat 8\n","    lst_array = median_lst.reduceRegion(\n","      reducer=ee.Reducer.toList(),\n","      geometry=LST.roi,\n","      scale=scale,\n","      maxPixels=1e8\n","    ).get('ST_B10')\n","\n","    # Step 4: Convert the result to a Python list\n","    lst_list = lst_array.getInfo()\n","\n","    # Convert to Celsius (Landsat 8 provides temperature in Kelvin)\n","    lst_list_celsius = [value * 0.00341802 + 149 - 273.15 for value in lst_list]  # Convert to Celsius\n","\n","    # Print the result\n","    return(lst_list_celsius)\n","\n","class DifferentialAnalysis(BaseAnalysis):\n","  def dataPrep(self):\n","    for i in range(0, len(self.LST), 2): # We will take two LST consecutive LST data and subtract to find the differences in temp.\n","      curr = self.LST[i] # Obtain current LST\n","      curr1 = self.LST[i+1] # Next LST\n","      # Process both\n","      info = curr.process()\n","      info1 = curr1.process()\n","      a1 = np.array(info1[0].reduceRegion( # Identical code to BaseAnalysis\n","          reducer=ee.Reducer.toList(),\n","          geometry=self.map.roi,\n","          scale=100,\n","          maxPixels=1e13\n","      ).get('ST_B10').getInfo())\n","      a2 = np.array(info[0].reduceRegion(\n","          reducer=ee.Reducer.toList(),\n","          geometry=self.map.roi,\n","          scale=100,\n","          maxPixels=1e13\n","      ).get('ST_B10').getInfo())\n","\n","      size = min(a1.size, a2.size) # Truncate size\n","      a1 = a1[:size]\n","      a2 = a2[:size]\n","\n","      # Subtract\n","      a = np.array(a1 - a2)\n","      self.dataSets.append(a) # Add to data set\n","      self.dates.append(f'{info1[1]} - {info[1]}')\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":17},"id":"1ryJ6A3efBpn","executionInfo":{"status":"ok","timestamp":1740322757244,"user_tz":300,"elapsed":30,"user":{"displayName":"TeamMcPro","userId":"15171345446647417487"}},"outputId":"ef6c5a99-7e70-492b-b4f1-394d9097aa8e"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","            <style>\n","                .geemap-dark {\n","                    --jp-widgets-color: white;\n","                    --jp-widgets-label-color: white;\n","                    --jp-ui-font-color1: white;\n","                    --jp-layout-color2: #454545;\n","                    background-color: #383838;\n","                }\n","\n","                .geemap-dark .jupyter-button {\n","                    --jp-layout-color3: #383838;\n","                }\n","\n","                .geemap-colab {\n","                    background-color: var(--colab-primary-surface-color, white);\n","                }\n","\n","                .geemap-colab .jupyter-button {\n","                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n","                }\n","            </style>\n","            "]},"metadata":{}}]},{"cell_type":"markdown","source":["Example run of the code. Uses the Map object defined above."],"metadata":{"id":"wj8lD9CnTaFY"}},{"cell_type":"code","source":["# Example use\n","analysis = BaseAnalysis(Map)\n","analysis.visualize([\"Comparison of Landsat 8 LST Histograms\", \"Temperature (°C)\", \"Frequency\"])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":564},"id":"G-gJfMNvgKtt","executionInfo":{"status":"ok","timestamp":1740322765450,"user_tz":300,"elapsed":2762,"user":{"displayName":"TeamMcPro","userId":"15171345446647417487"}},"outputId":"c05a50c9-138a-4a3e-cba1-be1e9ebde5ca"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","            <style>\n","                .geemap-dark {\n","                    --jp-widgets-color: white;\n","                    --jp-widgets-label-color: white;\n","                    --jp-ui-font-color1: white;\n","                    --jp-layout-color2: #454545;\n","                    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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["## Acknowledgements\n","\n","This code is adopted from Dr. Bo Zhao's code on studying UHI data in LA. His contributions served as a valuable starting point, and I have built upon and modified the original code to develop my own implementation. I truly appreciate his insights and the influence his work has had on this project.\n","\n"],"metadata":{"id":"-9H9GxjlxSY8"}}]}